Prescribed opioids are the most common analgesic used for alleviating acute and chronic pain. Despite this positive attribute, opioids are also highly abused drugs that can lead to tolerance and dependence. This abuse has caused a dramatic increase in opioid overdose-related deaths over the past couple of decades, deeming it a crisis. However, cessation of opioid use in tolerant individuals who wish to detoxify can precipitate severe withdrawal symptoms, often leading to relapse in order to avoid experiencing these negative symptoms. In recent studies, modulation of neuropathic and neuroinflammatory responses have been linked to withdrawal symptoms. As a result, we hypothesized that microglia, the resident immune cell of the central nervous system, serve as a potential target for withdrawal treatment. In order to test this, we reduced microglial activity by inhibiting the purinergic signaling pathway. This was achieved by first exposing mice to escalating doses of fentanyl over the course of a few days to create tolerance. Then, we administered clopidogrel, a selective antagonist of the P2Y12 receptors which are expressed in microglia, before inducing withdrawal using naloxone. Subsequently, in order to quantify whether inhibition of microglial P2Y12 receptors mitigated naloxone-precipitated withdrawal in fentanyl-tolerant mice, we measured avoidance of the withdrawal context with the conditioned place aversion (CPA) test, and evaluated somatic signs of withdrawal with EthoVision video analysis. Avoidance of the negative emotional and physical symptoms of withdrawal is a key driver of relapse, therefore the results from this experiment can provide prospective molecular pathways to target for future studies in treating opioid withdrawal symptoms. Reducing the severity of withdrawal would thus allow ease in discontinuing opioid use and diminish relapse.

Opioid abuse leads to over 40,000 annual deaths in the United States; even more individuals are impacted by anticipatory withdrawal anxiety and subsequent treatment avoidance. Despite the magnitude of this issue, there is a lack of effective treatments that address opioid dependence and withdrawal. Molecular responses to opioids have traditionally been linked to neuronal activity, but recent literature suggests that microglia also play a role in opioid addiction. Recent experiments we conducted reveal that opioid dependence and withdrawal have inverse effects on the microglia translatome, with morphine treatment correlating to decreased gene expression and withdrawal correlating to increased gene expression. We found a dramatic change in genes relating to cyclic AMP signaling during withdrawal, which has been shown to modulate microglia motility and potentially their interactions with nearby neurons. From this, we sought to further investigate the molecular basis of microglia morphology during opioid tolerance and withdrawal. To do so, we constructed four experimental groups consisting of mice who received saline followed by saline, saline followed by naloxone, morphine followed by saline, and morphine followed by naloxone. After obtaining the mouse brains through perfusion, we took sections of the striatum. In order to visualize and quantify microglia morphology, we performed 1ba1 immunohistochemistry to stain the slices, then imaged mounted slices on a confocal microscope to acquire confocal stacks of the striatum. This was followed by 3D reconstruction of individual microglia for analysis using 3DMorph software. The results of this experiment are a step towards clarification of molecular mechanisms behind opioid dependence and withdrawal for future work on mitigating the effects of opioid addiction. Alleviating withdrawal symptoms through translational research would allow users to more easily cease opioid use and therefore reduce opioid abuse mortality.

Infection is the most common cause of mortality among patients with multiple myeloma undergoing autologous stem-cell transplant. Understanding the risk factors that lead to higher probability of infection is instrumental in their prevention. Although some risk factors are known, there are no robust predictive models for infection among myeloma patients receiving a hematopoietic stem-cell transplant. In this research study, we compared a variety of machine learning algorithms to reveal clinical features pre-transplant that are most predictive of infection post-transplant. Using features extracted from the electronic health record by means of natural language processing and manual abstraction, we trained our model using patients diagnosed with multiple myeloma who received an autologous stem-cell transplant at the Seattle Cancer Care Alliance. Along with being robust to missing data, the model is capable of processing diverse, heterogeneous data and identifying a class of predictive factors that can guide treatment decisions. Preliminary results point to lab tests such as lymphocyte, neutrophil, and platelet counts as the strongest predictive features for infection, and further exploration is underway to understand the full impact of these and other features.

The CRISPR-Cas system is an adaptive immune system that provides protection to bacteria against bacteriophages. This system offers a clear advantage for bacteria as they need to defend themselves against the threat of bacteriophages. However, bacteria that have CRISPR systems can experience autoimmune damage, if they take up a spacer targeting a lysogen incorporated into their genome. This threat gives bacteria an incentive to encode CRISPR inhibitors. Through a metagenomics screen to identify novel inhibitors of the CRISPR-Cas9 system, it was found that a bacterial nickel binding chaperone protein, HypA, inhibits Cas9. This unexpected discovery may offer insight into how bacteria regulate their CRISPR systems and prevent autoimmunity. Through my work, I have determined that a diverse set of homologs can inhibit Cas9. I have also determined that HypA may act to destabilize Cas9’s structure. In order to investigate whether HypA can prevent autoimmune damage, I co-expressed HypA and a self-targeting Cas9, and found that HypA can protect the bacterial genome from Cas9. This work may provide insight into the evolution and regulation of the CRISPR-Cas system in bacteria, by helping explain how bacteria navigate the challenges caused by having an immune system. This work may also illuminate why some bacteria retain and others lose their CRISPR systems when they encode self-targeting CRISPR spacers.